Spatio-Temporal Variational Gaussian Processes
Authors: Oliver Hamelijnck, William Wilkinson, Niki Loppi, Arno Solin, Theodoros Damoulas
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We examine the scalability and performance of ST-VGP and its variants. Throughout, we use a Matérn-3/2 kernel and optimise the hyperparameters by maximising the ELBO using Adam [32]." and "Table 1: NYC-CRIME (small) results. ST-SVGP = SVGP when Z is fixed. TRAIN Z MODEL RMSE NLPD ST-SVGP 3.02 0.13 1.72 0.04 SVGP 3.02 0.13 1.72 0.04 ST-SVGP 2.79 0.15 1.64 0.04 SVGP 2.94 0.12 1.65 0.05 |
| Researcher Affiliation | Collaboration | Oliver Hamelijnck The Alan Turing Institute / University of Warwick ohamelijnck@turing.ac.uk William J. Wilkinson Aalto University william.wilkinson@aalto.fi Niki A. Loppi NVIDIA nloppi@nvidia.com Arno Solin Aalto University arno.solin@aalto.fi Theodoros Damoulas The Alan Turing Institute / University of Warwick tdamoulas@turing.ac.uk |
| Pseudocode | Yes | Algorithm 1 Spatio-temporal sparse VGP" and "Algorithm 2 Sparse spatio-temporal smoothing |
| Open Source Code | Yes | We provide JAX code for all methods at https://github.com/Aalto ML/spatio-temporal-GPs. |
| Open Datasets | Yes | NYC-CRIME Count Dataset We model crime numbers across New York City, USA (NYC), using daily complaint data from [1]. [1] 2014 2015 crimes reported in all 5 boroughs of New York City. https://www.kaggle.com/ adamschroeder/crimes-new-york-city." and "Using hourly data from the London air quality network [29] between January 2019 and April 2019... [29] Imperial College London. Londonair London air quality network (LAQN). https://www.londonair. org.uk, 2020. |
| Dataset Splits | Yes | We use 5-fold cross-validation (i.e., 80 20 train-test split), train for 500 iterations (except for AIR-QUALITY where we train for 300) and report RMSE, negative log predictive density (NLPD, see App. K.1) and average per-iteration training times on CPU and GPU. |
| Hardware Specification | No | The paper mentions running experiments on 'CPU and GPU' and refers to 'computational resources provided by the Aalto Science-IT project and CSC IT Center for Science, Finland', but does not provide specific hardware details like CPU or GPU models. |
| Software Dependencies | No | The paper mentions 'JAX code' but does not specify version numbers for JAX or any other software libraries required for replication. |
| Experiment Setup | Yes | We use learning rates of ρ = 0.01, β = 1 in the conjugate case, and ρ = 0.01, β = 0.1 in the non-conjugate case. We train for 500 iterations (except for AIR-QUALITY where we train for 300) and report RMSE, negative log predictive density (NLPD, see App. K.1) and average per-iteration training times on CPU and GPU. ... SVGP with 2000, 2500, 5000, and 8000 inducing points with mini-batch sizes of 600, 800, 2000, and 3000 respectively. |